dc.creator | Nanas, N. | en |
dc.creator | Vavalis, M. | en |
dc.creator | De Roeck, A. | en |
dc.date.accessioned | 2015-11-23T10:40:29Z | |
dc.date.available | 2015-11-23T10:40:29Z | |
dc.date.issued | 2010 | |
dc.identifier | 10.1007/s11721-010-0044-6 | |
dc.identifier.issn | 1935-3812 | |
dc.identifier.uri | http://hdl.handle.net/11615/31269 | |
dc.description.abstract | Jerne's idiotypic network theory stresses the importance of antibody-to-antibody interactions and provides possible explanations for self-tolerance and increased diversity in the immune repertoire. In this paper, we use an immune network model to build a user profile for adaptive information filtering. Antibody-to-antibody interactions in the profile's network model correlations between words in text. The user profile has to be able to represent a user's multiple interests and adapt to changes in them over time. This is a complex and dynamic engineering problem with clear analogies to the immune process of self-assertion. We present a series of experiments investigating the effect of term correlations on the user's profile performance. The results show that term correlations can encode additional information, which has a positive effect on the profile's ability to assess the relevance of documents to the user's interests and to adapt to changes in them. | en |
dc.source | Swarm Intelligence | en |
dc.source.uri | <Go to ISI>://WOS:000286336200003 | |
dc.subject | Immune network | en |
dc.subject | Autopoiesis | en |
dc.subject | Information filtering | en |
dc.subject | ARTIFICIAL IMMUNE-SYSTEM | en |
dc.subject | CLONAL SELECTION | en |
dc.subject | NETWORK THEORY | en |
dc.subject | SELF | en |
dc.subject | EVOLUTION | en |
dc.subject | MODELS | en |
dc.subject | SIZE | en |
dc.subject | SPAM | en |
dc.subject | Computer Science, Artificial Intelligence | en |
dc.subject | Mathematics, Applied | en |
dc.subject | Robotics | en |
dc.title | Words, antibodies and their interactions | en |
dc.type | journalArticle | en |